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Google Professional-Cloud-Architect Exam Syllabus Topics:

Section	Objectives
Analyzing and optimizing technical and business processes	- Analyzing and defining technical processes 1. Incident management and troubleshooting 2. CI/CD pipeline design 3. Software development lifecycle (SDLC) - Analyzing and defining business processes 1. Cost management and optimization 2. Business KPIs and metrics 3. Stakeholder communication
Designing and planning a cloud solution architecture	- Designing solution infrastructure that meets business requirements 1. Migration planning 2. Integration with existing systems 3. Cost optimization 4. Business use cases and product strategy 5. Data and storage architecture 6. Networking and compute resources - Enabling future cloud technology 1. Designing for scalability and elasticity 2. Implementing future-proof architectures - Creating a migration plan 1. Refactoring and rearchitecting applications 2. Planning data migration 3. Identifying workloads for migration - Designing network, storage, and compute resources 1. Network design including load balancing and firewall rules 2. GKE cluster and workload design 3. Storage options and data lifecycle management 4. Compute engine selection and configuration
Managing implementation	- Interacting with Google Cloud programmatically 1. Infrastructure as Code (IaC) 2. Cloud SDK and gcloud CLI 3. APIs and client libraries - Advising development/operation team(s) to ensure successful deployment 1. Deployment strategies 2. Application development best practices 3. Testing and validation
Managing and provisioning a solution infrastructure	- Configuring individual storage systems 1. Cloud Storage configuration 2. Cloud SQL and Spanner 3. Datastore and Bigtable - Configuring network topologies 1. Hybrid and multi-cloud connectivity 2. VPC design and peering 3. Private access and VPN configuration - Configuring compute systems 1. Compute Engine configuration 2. GKE configuration and management 3. Cloud Run and Cloud Functions
Ensuring solution and operations reliability	- Monitoring/logging/profiling/alerting solution 1. Cloud Monitoring and Cloud Logging 2. Error reporting and alerting 3. Performance profiling and tracing - Assisting with support 1. Incident response and troubleshooting 2. Capacity planning and scaling - Evaluating quality control measures - Deployment and release management 1. Release management and versioning 2. Deployment strategies and rollback
Designing for security and compliance	- Designing for security 1. Identity and Access Management (IAM) 2. Data security and encryption 3. Network security 4. Application security - Designing for compliance 1. Regulatory and compliance requirements 2. Data residency and sovereignty 3. Audit logging and monitoring

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q316-Q321):

NEW QUESTION # 316
For this question, refer to the EHR Healthcare case study. EHR has single Dedicated Interconnect connection between their primary data center and Googles network. This connection satisfies EHR's network and security policies:
* On-premises servers without public IP addresses need to connect to cloud resources without public IP addresses
* Traffic flows from production network mgmt. servers to Compute Engine virtual machines should never traverse the public internet.
You need to upgrade the EHR connection to comply with their requirements. The new connection design must support business critical needs and meet the same network and security policy requirements. What should you do?

A. Add three new Cloud VPN connections
B. Add a new Carrier Peering connection
C. Upgrade the bandwidth on the Dedicated Interconnect connection to 100 G
D. Add a new Dedicated Interconnect connection

Answer: D

Explanation:
Explanation
The case does not call out the throughput being an issue. However, to achieve 99.99%, you need to have 4 connections as per Google recommendations. However, in the options only A has the option to add an additional Interconnect connection.
https://cloud.google.com/network-connectivity/docs/interconnect/concepts/dedicated-overview#availability

NEW QUESTION # 317
You need to build a continuous delivery pipeline tor a containerized application in Google Cloud. You want to run all your tests in the pipeline to Improve your application ' s quality. What should you do?

A. 1. Alter the developers push the code to a central repository, trigger Cloud Build to build the application container. Then run unit tests
2. If unit tests are successful, deploy the container to a testing environment and run integration tests.
3. If the integration tests are successful, deploy the container to a production environment and run acceptance tests.
B. 1. After the developers push the code to a central repository, trigger Cloud Build to run unit tests If all unit tests are successful, build the application container and push it to a central registry.
2. Trigger Cloud Build to deploy the container to a testing environment and run integration tests and acceptance tests
3. If all tests are successful, deploy the application to the production environment and run the smoke tests
C. 1. Automatically run unit tests in a local environment once the code is changed It all tests are successful, build a container.
2. Trigger Cloud Build to deploy the container to a testing environment, and run integration tests and acceptance tests.
3. If all tests are successful, tag the code as production ready. Trigger Cloud Build to build and deploy the container to the production environment.
D. 1. Run unit tests in the developer ' s local environment before committing and pushing the code to a central repository
2. After the code is pushed, trigger Cloud Build to build the application container and deploy the container to a testing environment, and run integration tests
3. If the integration tests are successful, deploy the container to your production environment and run acceptance tests.

Answer: B

Explanation:
The most efficient pipeline follows the " Fail Fast " principle (Option A). You should run unit tests before the expensive and time-consuming process of building a container image. If the code logic is broken, the build should fail immediately. Once the image is built and stored, it is deployed to a Testing Environment for integration and acceptance tests to ensure it works within the system architecture. Finally, after deploying to Production , you run smoke tests (basic health checks) to ensure the live environment is stable. Options C and D are less ideal because they either suggest running acceptance tests after reaching production or skipping local validation steps, which increases the risk of deploying broken code to users.

NEW QUESTION # 318
Your applications will be writing their logs to BigQuery for analysis. Each application should have its own table. Any logs older than 45 days should be removed. You want to optimize storage and follow Google- recommended practices. What should you do?

A. Rely on BigQuery's default behavior to prune application logs older than 45 days
B. Make the tables time-partitioned, and configure the partition expiration at 45 days
C. Configure the expiration time for your tables at 45 days
D. Create a script that uses the BigQuery command line tool (bq) to remove records older than 45 days

Answer: C

NEW QUESTION # 319
You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address. You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly. What should you do?

A. Ensure that a firewall rule exists to allow source traffic on HTTP/HTTPS to reach the load balancer.
B. Create a tag on each instance with the name of the load balancer. Configure a firewall rule with the name of the load balancer as the source and the instance tag as the destination.
C. Assign a public IP to each instance and configure a firewall rule to allow the load balancer to reach the instance public IP.
D. Ensure that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group.

Answer: D

Explanation:
The best practice when configuration a health check is to check health and serve traffic on the same port. However, it is possible to perform health checks on one port, but serve traffic on another. If you do use two different ports, ensure that firewall rules and services running on instances are configured appropriately. If you run health checks and serve traffic on the same port, but decide to switch ports at some point, be sure to update both the backend service and the health check.
Backend services that do not have a valid global forwarding rule referencing it will not be health checked and will have no health status.
References: https://cloud.google.com/compute/docs/load-balancing/http/backend-service

NEW QUESTION # 320
A production database virtual machine on Google Compute Engine has an ext4-formatted persistent disk for data files The database is about to run out of storage space How can you remediate the problem with the least amount of downtime?

A. In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.
B. In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk.
C. In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux.
D. In the Cloud Platform Console, create a snapshot of the persistent disk, restore the snapshot to a new larger disk, unmount the old disk, mount the new disk, and restart the database service.
E. Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine.

Answer: A

Explanation:
On Linux instances, connect to your instance and manually resize your partitions and file systems to use the additional disk space that you added.
Extend the file system on the disk or the partition to use the added space. If you grew a partition on your disk, specify the partition. If your disk does not have a partition table, specify only the disk ID.
sudo resize2fs /dev/[DISK_ID][PARTITION_NUMBER]
where [DISK_ID] is the device name and [PARTITION_NUMBER] is the partition number for the device where you are resizing the file system.
References: https://cloud.google.com/compute/docs/disks/add-persistent-disk

NEW QUESTION # 321
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